{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C has no label.\n",
      " Volume Serial Number is 3C6A-BEA6\n",
      "\n",
      " Directory of C:\\Users\\intel\\python code\\Api\\AI & ML project\\Meal delivery company\n",
      "\n",
      "07/14/2020  07:49 PM    <DIR>          .\n",
      "07/14/2020  07:49 PM    <DIR>          ..\n",
      "07/10/2020  10:55 PM    <DIR>          .ipynb_checkpoints\n",
      "07/10/2020  10:46 PM             1,661 fulfilment_center_info.csv\n",
      "07/14/2020  07:49 PM            82,092 Meal delivery company.ipynb\n",
      "07/10/2020  10:46 PM             1,096 meal_info.csv\n",
      "07/11/2020  12:21 AM               510 one_to_ten_week_data.csv\n",
      "07/10/2020  11:33 PM             8,858 one_to_ten_week_data.xlsx\n",
      "07/10/2020  10:46 PM         1,239,576 test.csv\n",
      "07/10/2020  10:46 PM        18,733,819 train.csv\n",
      "               7 File(s)     20,067,612 bytes\n",
      "               3 Dir(s)  76,067,708,928 bytes free\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn as svm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1379560</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1466964</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1993</td>\n",
       "      <td>136.83</td>\n",
       "      <td>135.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1346989</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2539</td>\n",
       "      <td>134.86</td>\n",
       "      <td>135.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1338232</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2139</td>\n",
       "      <td>339.50</td>\n",
       "      <td>437.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1448490</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2631</td>\n",
       "      <td>243.50</td>\n",
       "      <td>242.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1379560     1         55     1885          136.83      152.29   \n",
       "1  1466964     1         55     1993          136.83      135.83   \n",
       "2  1346989     1         55     2539          134.86      135.86   \n",
       "3  1338232     1         55     2139          339.50      437.53   \n",
       "4  1448490     1         55     2631          243.50      242.50   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  num_orders  \n",
       "0                      0                  0         177  \n",
       "1                      0                  0         270  \n",
       "2                      0                  0         189  \n",
       "3                      0                  0          54  \n",
       "4                      0                  0          40  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x83e7b6f848>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df['week']\n",
    "y=df['num_orders']\n",
    "\n",
    "plt.plot(x, y, 'r')\n",
    "plt.xlabel('Number of weeks')\n",
    "plt.ylabel('Total number of orders')\n",
    "plt.title('sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x83e7d76048>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.54511e+05, 1.75500e+03, 2.08000e+02, 4.80000e+01, 1.80000e+01,\n",
       "        6.00000e+00, 1.00000e+00, 0.00000e+00, 0.00000e+00, 1.00000e+00]),\n",
       " array([1.30000e+01, 2.44160e+03, 4.87020e+03, 7.29880e+03, 9.72740e+03,\n",
       "        1.21560e+04, 1.45846e+04, 1.70132e+04, 1.94418e+04, 2.18704e+04,\n",
       "        2.42990e+04]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x83eeb59548>,\n",
       "  <matplotlib.lines.Line2D at 0x83eeb42888>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x83eeb59f48>,\n",
       "  <matplotlib.lines.Line2D at 0x83eeb5de08>],\n",
       " 'boxes': [<matplotlib.patches.PathPatch at 0x83eeb53f08>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x83eeb5df08>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x83eeb62dc8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(y,vert=True,patch_artist=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x83eebbbfc8>,\n",
       "  <matplotlib.lines.Line2D at 0x83eebbf988>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x83eebbfa88>,\n",
       "  <matplotlib.lines.Line2D at 0x83eebc4908>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x83eebbb808>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x83eebc4f88>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x83eebc9888>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['checkout_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x83eec23b88>,\n",
       "  <matplotlib.lines.Line2D at 0x83eec23c88>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x83eec27b48>,\n",
       "  <matplotlib.lines.Line2D at 0x83eec27c48>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x83eec23208>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x83eec2bac8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x83eec2bbc8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['base_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x83eec65208>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_df=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_x=all_df['week']\n",
    "# all_y=all_df['num_orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.bar(all_x,all_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(all_x,all_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(all_x,all_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x83e798e208>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.fill_between(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.209504e+06</td>\n",
       "      <td>5.50000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>140.974000</td>\n",
       "      <td>141.074000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.287128e+05</td>\n",
       "      <td>3.02765</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.011933</td>\n",
       "      <td>4.972226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.816201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.007434e+06</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>133.860000</td>\n",
       "      <td>133.860000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.119658e+06</td>\n",
       "      <td>3.25000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>136.102500</td>\n",
       "      <td>136.102500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>959.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.195867e+06</td>\n",
       "      <td>5.50000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>143.560000</td>\n",
       "      <td>142.590000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>993.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.274214e+06</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>144.302500</td>\n",
       "      <td>145.302500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1046.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.425602e+06</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>146.530000</td>\n",
       "      <td>146.530000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1107.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id      week  center_id  meal_id  checkout_price  base_price  \\\n",
       "count  1.000000e+01  10.00000       10.0     10.0       10.000000   10.000000   \n",
       "mean   1.209504e+06   5.50000       11.0   1885.0      140.974000  141.074000   \n",
       "std    1.287128e+05   3.02765        0.0      0.0        5.011933    4.972226   \n",
       "min    1.007434e+06   1.00000       11.0   1885.0      133.860000  133.860000   \n",
       "25%    1.119658e+06   3.25000       11.0   1885.0      136.102500  136.102500   \n",
       "50%    1.195867e+06   5.50000       11.0   1885.0      143.560000  142.590000   \n",
       "75%    1.274214e+06   7.75000       11.0   1885.0      144.302500  145.302500   \n",
       "max    1.425602e+06  10.00000       11.0   1885.0      146.530000  146.530000   \n",
       "\n",
       "       emailer_for_promotion  homepage_featured   num_orders  \n",
       "count                   10.0               10.0    10.000000  \n",
       "mean                     0.0                0.0   999.300000  \n",
       "std                      0.0                0.0    71.816201  \n",
       "min                      0.0                0.0   891.000000  \n",
       "25%                      0.0                0.0   959.000000  \n",
       "50%                      0.0                0.0   993.000000  \n",
       "75%                      0.0                0.0  1046.750000  \n",
       "max                      0.0                0.0  1107.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x83e9a87d88>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x83ed55e4c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AGgCqR1XUNPJZUSVT0/rTJ0yvOfB1Q+KiWDQxifySGtbknLK7HNXD9C9S9aiNeaUAzBquI376i8mp/TheWcfGvFKSYiOYkBJrd0mqh+gegOoxtQ3NZB8tZ0JyLDEROt6/P7l23GBS4yL566fHKa6ut7sc1UO8CgARWSgiuSKSJyKPtLM8TERWWsu3iUhaq2WPWvNzReQqa164iGwXkc9EJEdEfthdHVK+Y2tBGU0thtm69e93XEHC4ilDCHEJr24/RmOz2+6SVA/oNABExAU8AVwNZAK3iEhmm2ZLgQpjTAbwOPCYtW4msBgYAywEnrTerwG43BgzAZgILBSR6d3TJeULmlrcbDlcxoiBfRgUE253OeoixESEcFNWCsVnGnhz9wm7y1E9wJs9gKlAnjEm3xjTCKwAFrVpswh4wZp+DZgvImLNX2GMaTDGFAB5wFTjcdZqH2J96SUHAeSTYxXUNLYwR7f+/drwgX2ZOzKBnUcr2F1UaXc5qpt5EwBJQGGr10XWvHbbGGOagSog7nzriohLRHYBxcBaY8y29n64iNwjItkikl1SUuJFucpubmPYeMhzAjE9PsruclQXzR81kJR+Ebyx6ziVtY12l6O6kTcB0N44sW231jtq0+G6xpgWY8xEIBmYKiJj2/vhxpinjTFZxpishATdmvQH+06coaymkTkjEhAdZtjvuYI8w0W4Dfx5ZxFuozvrgcKbACgCUlq9TgbaHhD8vI2IBAMxQLk36xpjKoEP8JwjUH7OGMPHh0roHxXKmMRou8tR3SSuTxhfHJ9IQWkNm6xLe5X/8yYAdgDDRSRdRELxnNRd3abNamCJNX0DsN4YY6z5i62rhNKB4cB2EUkQkVgAEYkArgAOdL07ym5HymoprKhjVka8PmQkwFwyJJbMwdGs3Xea4jN6aWgg6DQArGP6DwBrgP3AKmNMjoj8SESus5otB+JEJA94GHjEWjcHWAXsA94FlhljWoDBwAYR2Y0nYNYaY/7evV1Tdvj4UAmRoS4uGaKPeww0IsKiiYmEBgfx+id6KCgQeHUnsDHmbeDtNvO+32q6Hrixg3V/AvykzbzdwKQLLVb5tpLqBg6cquZyfdh7wOobHsIXJySyckchGw+VMmeEnpfzZ/pXqrrN5sOluIJEH/gS4MYnxTAmMZp1+09TpkNH+zUNANUtahub+eRYBRNTYukbrsM+BDIR4QvjEwkKEt7cfQKjh4L8lgaA6hY7CsppajHMHKZDPjtBTEQIV44eyMHTZ9l74ozd5aiLpAGguqzZ7WZLfhkZCTrsg5NMHxpHYkw4b+0+QX1Ti93lqIugAaC6bO/xKs7UNzMzI87uUlQvcgUJiyYmUV3fzLr9p+0uR10EDQDVJcYYNuaVktAnjOED+9pdjuplKf0jmZreny2HyzheWWd3OeoCaQCoLjlSVsuJynpmZMTpjV8OtSBzEFFhwfxt13G9N8DPaACoLtmUV0pEiItJKXrjl1NFhLq4Ztxgiirq2F5Qbnc56gJoAKiLVna2gf0nzzAtvb/e+OVwE5JjGBofxbr9p6lr1BPC/kL/atVF25xfRpAI04fqyV+nExGuGTeYusYWNuQW212O8pIGgLoodY0t7DxSwfjkGKL1eb8KSIyN4JLUfmw5XKZ3CPsJDQB1UbKPltPY4mZmht74pf7PlaMH4goS1uScsrsU5QUNAHXBWtyGLYfLSI+PIjE2wu5ylA+Jjghhzoh49p44w44jekLY12kAqAuWc6KKyromZunWv2rHrIwEosOD+fHf9+F262WhvkwDQF2wTXmlxEWFMnKQ3vil/llocBBXjRnEZ0VVrP6s7cMDlS/RAFAXZOfRCgor6pgxTG/8Uh2bkBLLuKQYfvbuAR0nyIdpAKgL8tzGAsJDgrgkVW/8Uh0LEuE/rh3Niap6nttUYHc5qgMaAMprheW1vLP3JFPT+hMW7LK7HOXjpg2NY/6oATz1wWGq6prsLke1QwNAee2FzUcQvfFLXYBvLhjJmfpmnvko3+5SVDs0AJRXzjY0s3JHIdeMG0xsZKjd5Sg/kZkYzRcnJPLcpgJKqvXmMF+jAaC8smpHIdUNzSydlW53KcrPPHzlCBqa3TyxIc/uUlQbGgCqU80tbp7bVEBWaj8mpsTaXY7yM+nxUdyUlcwr245RVFFrdzmqFQ0A1an39p2mqKKOr80eancpyk/96+XDQeA37x+yuxTVigaA6tSzH+eTGhfJlZkD7S5F+anE2AjumJ7KazuLOFxy1u5ylEUDQJ3XzqMVfHKskq/OTMcVpDd+qYt3/7xhRIS4+OV7B+0uRVk0ANR5Ld+YT3R4MDdMTra7FOXn4vqEsXT2UN7ac5K9x6vsLkehAaDOo7C8lnf3nuK26alEhQXbXY4KAF+bnU50eLCeC/ARGgCqQ89tKiBIhCWXptldigoQ0eEhLJ01lPf2nda9AB/gVQCIyEIRyRWRPBF5pJ3lYSKy0lq+TUTSWi171JqfKyJXWfNSRGSDiOwXkRwRebC7OqS6R1VdE6t2FHLdhEQGxYTbXY4KIHfNTNO9AB/RaQCIiAt4ArgayARuEZHMNs2WAhXGmAzgceAxa91MYDEwBlgIPGm9XzPwTWPMaGA6sKyd91Q2WrH9GDWNLSydrTd+qe4VE6F7Ab7Cmz2AqUCeMSbfGNMIrAAWtWmzCHjBmn4NmC8iYs1fYYxpMMYUAHnAVGPMSWPMJwDGmGpgP5DU9e6o7tDU4uaPm48wY1gcYxJj7C5HBSDdC/AN3gRAElDY6nUR//xh/XkbY0wzUAXEebOudbhoErCtvR8uIveISLaIZJeUlHhRruqqt/ec5GRVPV/TrX/VQ3QvwDd4EwDtXfzd9jlvHbU577oi0gd4HXjIGHOmvR9ujHnaGJNljMlKSEjwolzVFcYYnvk4n2EJUcwbMcDuclQA070A+3kTAEVASqvXyUDb57x93kZEgoEYoPx864pICJ4P/z8ZY/5yMcWr7retoJy9x8+wdNZQgvTGL9WDdC/Aft4EwA5guIiki0gonpO6q9u0WQ0ssaZvANYbY4w1f7F1lVA6MBzYbp0fWA7sN8b8sjs6orrHsx8X0D8qlOsv0VMyqufpXoC9Og0A65j+A8AaPCdrVxljckTkRyJyndVsORAnInnAw8Aj1ro5wCpgH/AusMwY0wLMBO4ALheRXdbXNd3cN3WB8kvO8v6B09w+PZXwEH3il+p5uhdgL69u7zTGvA283Wbe91tN1wM3drDuT4CftJm3kfbPDygbLd9YQIgriDump9pdinKQu2amsXxjPr95/xBP35lldzmOoncCKwBKqhv4884irp+URELfMLvLUQ6iewH20QBQADy/qYCmFjf3zh1mdynKgfRcgD00ABRn6pt4actRrhk7mPT4KLvLUQ6kewH20ABQvLLtGNUNzXxdt/6VjXQvoPdpADhcfVMLyzcWMHt4POOSddgHZR/dC+h9GgAO95dPjlNS3cB9uvWvfIDuBfQuDQAHa3Eb/vDRYcYnx3DpsDi7y1FK9wJ6mQaAg72z9yRHy2q5b+4wPDdnK2U/3QvoPRoADmWM4akPDzM0PooFYwbZXY5Sn9O9gN6jAeBQHx8qZe/xM9w7dyguHfRN+Zi7Z3n2An617qDdpQQ0DQAHMsbw2/WHGBQdzpcm6aBvyvdEh4dwz5yhrNtfzGeFlXaXE7A0ABxo8+Eydhyp4P7LhhEWrIO+Kd9018x0YiNDdC+gB2kAOIwxhl+v82z935SV0vkKStmkT1gw984ZxobcEj45VmF3OQFJA8BhthwuY/uRcu6/bJgO+ax83p2XptI/KpTH1+peQE/QAHAQYwy/WneIgdFhuvWv/EJUWDBfnzuUjw+Vkn2k3O5yAo4GgINsybe2/udl6Na/8ht3TE8jvk8Yj+u5gG6nAeAg57b+b56iW//Kf0SEuvj63KFsyitja36Z3eUEFA0Ah9hyuIztBeXcN1eP/Sv/c/v0VAb0DeN/1+Tiedy46g4aAA7xq3UHGdA3jMVTh9hdilIXLDzExTfmDyf7aAXrDxTbXU7A0ABwgM15pWwrKOe+ebr1r/zXzVNSSIuL5Gfv5tLi1r2A7qABEODcbsNP3z1AYkw4t+jWv/JjIa4gvrlgJLmnq/nbruN2lxMQNAAC3Ft7TrK7qIqHF4zUrX/l964dN5ixSdH84r2DNDS32F2O39MACGCNzW5+viaXUYP68mUd80cFgKAg4TsLR3G8so4/bT1mdzl+TwMggL26/RjHymv5ztWjdMRPFTBmD09gZkYcv9uQx9mGZrvL8WsaAAGqur6J37x/iEuHxjFvRILd5SjVrb591SjKaxp55qN8u0vxaxoAAeqZj/Ipq2nk0WtG6dO+VMCZkBLLNeMG8ezH+ZSebbC7HL+lARCAis/U88zHBXxh/GDGJ8faXY5SPeJbC0ZS3+zmlzpQ3EXTAAhAv3r/EE0tbr61YKTdpSjVY4Ym9OHOS1N5dfsxck7ooyMvhlcBICILRSRXRPJE5JF2loeJyEpr+TYRSWu17FFrfq6IXNVq/nMiUiwie7ujI8rj4OlqVu4o5LZpQ0iLj7K7HKV61ENXjKBfZCg/XL1Ph4i4CJ0GgIi4gCeAq4FM4BYRyWzTbClQYYzJAB4HHrPWzQQWA2OAhcCT1vsB/NGap7qJMYYfrM6hT1gwD10xwu5ylOpxMREhfGvBSLYfKefvu0/aXY7f8WYPYCqQZ4zJN8Y0AiuARW3aLAJesKZfA+aL58zjImCFMabBGFMA5FnvhzHmI0AH+O5G7+49xebDZXxrwQj6RYXaXY5SveLmKSlkDo7mf97eT12j3hx2IbwJgCSgsNXrImteu22MMc1AFRDn5bqqG9Q1tvDjt/YzalBfHfJBOYorSPjBdWM4UVXPUx8etrscv+JNALR3DWHbg20dtfFm3fP/cJF7RCRbRLJLSkouZFVHefKDPI5X1vGD68YQ7NJz+8pZpqb35wvjB/PUh4cpqqi1uxy/4c0nRRHQ+gkiycCJjtqISDAQg+fwjjfrnpcx5mljTJYxJishQW9oak9ecTVPfXiYL09KYvrQOLvLUcoW371mNCLwP28fsLsUv+FNAOwAhotIuoiE4jmpu7pNm9XAEmv6BmC98ZySXw0stq4SSgeGA9u7p3QFnhO/3/3rXiJDg/netaPtLkcp2yTGRnDf3Aze2nOSjYdK7S7HL3QaANYx/QeANcB+YJUxJkdEfiQi11nNlgNxIpIHPAw8Yq2bA6wC9gHvAsuMMS0AIvIqsAUYKSJFIrK0e7vmDH/eWcT2gnIevXoU8X3C7C5HKVvdO3co6fFRfPeve/SEsBfEn66dzcrKMtnZ2XaX4TOKz9Rz5eMfMWJgH1becylBvTTg2yvbdBRG1blbp9lzMcLmw6Xc+sw27ps3jO8sHGVLDb5ERHYaY7LaW6ZnC/3UuUM/9U0t/PQr43vtw18pXzdjWDw3Tk7m6Y/y2XfijN3l+DQNAD/1t10nWLf/NN9aMJJhCX3sLkcpn/K9a0fTLzKEb7/+GU0tbrvL8VkaAH7o9Jl6/nN1DpNT+/HVWel2l6OUz4mNDOXHXxrL3uNneHKD3hvQEQ0AP+N2G7656jPP075uGK8PelGqAwvHDmbRxER+u/4Qe4/rYHHt0QDwM89uzGdjXin/+cVMhuqhH6XO64fXjaF/VCjfXPUZ9U16VVBbGgB+ZE9RFT9fk8vCMYO4eUpK5yso5XCxkaH87Ibx5J6u5r/f3m93OT5HA8BPVNU2cf8rO4nvE8ZPvzJOn/KllJfmjRzA12al8+KWo6zJOWV3OT5FA8APuN2Gf1u1i1NV9fzu1kuIjdSRPpW6EN9eOIpxSTF8+7XdHK+ss7scn6EB4Aee2JDH+gPF/Me1mUxO7Wd3OUr5ndDgIH57yyRa3Ib7Xt6p5wMsGgA+bk3OKX657iCLJiZy56WpdpejlN9Ki4/ilzdNYHdRFd//2159ghgaAD5t7/EqHlqxi/HJsTz2lfF63F+pLlowZhDfuDyDVdlFvKxDmmgA+KpTVfUsfWEH/aNCeebOyYSHuDpfSSnVqYeuGMHlowbwg9U5fJBbbHc5ttIA8EEVNY3csXwbNQ0tLL8riwF9w2Y2dREAAA0MSURBVO0uSamAERQk/OaWSYwc2Jdlf/qEnBPOvUlMA8DHVNc3seT57Rwrr+XZJVmMGhRtd0lKBZw+YcE8f/cUYiJCuPv5HRSWO/MpYhoAPqSmoZmlL2Sz78QZnrztEn26l1I9aGB0OM/fPZWGZje3PruVk1XOuzxUA8BHnKlv4s7ntrPzaAWP3zyR+aMH2l2SUgFv5KC+vLR0KpU1Tdz6zDaKq+vtLqlXaQD4gPKaRu54dhu7iyp54tZJfHFCot0lKeUY45Njef7uKZw+U8/Nf9jqqIfKawDYrKC0huuf3MT+U9X8/rbJLBw72O6SlHKcrLT+vLR0KmVnG7jh91vIK662u6ReoQFgo+0F5Vz/5CbO1Dfz6r9M54pMPeyjlF0mp/Zn5b2X0uw23PDUFjYfDvwHy2sA2MAYw7Mf53PLM1vpFxnKX+6boUM8KOUDRg+O5i/3zSC+Txh3Lt/Oy1uP2l1Sj9IA6GWVtY0se+UTfvzWfq4YPYA3HphJWnyU3WUppSxD4iL5y/0zmD08nv94Yy/fXPUZNQ3NdpfVI4LtLsBJNuQW853XdlNe08ijV4/injlDdXgHpXxQdHgIzy6Zwq/XHeS3G/L49FgFv7llEmOTYuwurVvpHkAvKK6u5+GVu7j7+R30iwzljWUzuXfuMP3wV8qHuYKEhxeM5JWvTaemsZlFT2zip+8cCKiRRHUPoAfVN7Xw0paj/Ob9QzQ0u3ngsgweuDxDx/VRyo9cOiyONQ/N4b/f3s9THx7mnb0n+e41o1mQOdDvN+I0AHpAQ3MLr+88zm/XH+JkVT1zRyTwg+vGkK7H+pXyS55HS07gS5OS+P7fcrj3pZ1MSevHv181iqnp/e0u76JpAHSj8ppGXt1+jD9uPkJJdQOXDInlFzdNYMaweLtLU0p1gxnD4nn3wdmsyi7il2sPctMftjAlrR9fnzuMeSMH4Aryrz0CDYAuampxsymvlD/vLGJtzmkaW9zMHh7PL26cwOzh8X6/i6iU+kfBriBunTaEL09KYuWOYzz9UT5LX8gmKTaCW6cN4UuTkkiKjbC7TK9oAFyEqromNueV8v6BYtbuO01VXROxkSHcOm0It0wdwshBfe0uUSnVwyJCXdw1M51bp6Wydt9p/rTtKD9fk8vP1+QyObUfV48dxLyRAxiWEOWzG4IaAJ1ocRvyS86yq7CS3UVVfFZUyd7jVbgN9A0P5srRA7l63GDmjIgnLFhP7irlNKHBQVw7fjDXjh/M0bIa/r77JG9+doIfv7WfH7+1n6TYCCYNiWViSiyThsQyJjHGZy4E8SoARGQh8GvABTxrjPlpm+VhwIvAZKAMuNkYc8Ra9iiwFGgBvmGMWePNe/aW5hY3lXVNVNQ0Unq2kcLyWo6W13CsvI5jZTXkFZ+lptFz2VefsGDGJcWw7LIM5oxIYGJKLCEuvZJWKeWRGhfFsssyWHZZBkUVtXx4sITNeWV8eqySv+8+CUBwkJAxoA+pcZEM6R/JkLgohvSPZHBMOLGRIcRGhBIa3DufK50GgIi4gCeAK4EiYIeIrDbG7GvVbClQYYzJEJHFwGPAzSKSCSwGxgCJwDoRGWGt09l7dpslz22ntrGZxmY3jS2GxuYWGprdVNU1UV3/z3f4uYKEpNgIUuMi+crkZCYkxzIhJYah8X0I8rOTPEopeyT3i+S2aancNi0V8NwP9FlhFbsKKzhwspr8kho+yC2hodn9T+tGhbqIjQwlKsxFeIiLhD5hLL9rSrfX6M0ewFQgzxiTDyAiK4BFQOsP60XAD6zp14Dfieeg1yJghTGmASgQkTzr/fDiPbuN2xhCXEFEhgYTGhxEqCuI0OAgYiJCiI0MoV9kKP2iQomLCiWlXySJseEE65a9UqobDegbzpWZ4VzZatBHt9tQXN3A0bIaiqsbqKxtpKK2iYraRiprm6hrbKG+uaXHDhl5EwBJQGGr10XAtI7aGGOaRaQKiLPmb22zbpI13dl7AiAi9wD3WC/PikiuFzXbJR4I/CEEO6b9d27//6nvt9lUiE16/P/+ydsvetXUjhZ4EwDtHfMwXrbpaH57m9dt39Mz05ingafPV6CvEJFsY0yW3XXYRfvv3P47ue/gv/335jhHEZDS6nUycKKjNiISDMQA5edZ15v3VEop1YO8CYAdwHARSReRUDwndVe3abMaWGJN3wCsN8YYa/5iEQkTkXRgOLDdy/dUSinVgzo9BGQd038AWIPnks3njDE5IvIjINsYsxpYDrxkneQtx/OBjtVuFZ6Tu83AMmNMC0B779n93et1fnGoqgdp/53LyX0HP+2/eDbUlVJKOY1e66iUUg6lAaCUUg6lAXABROTfRCRHRPaKyKsiEm6dyN4mIodEZKV1UhvrxPdKEcmzlqfZW33XiciDVt9zROQha15/EVlr9X+tiPSz5ouI/Mbq/24RucTe6i+ciDwnIsUisrfVvAvur4gssdofEpEl7f0sX9RB/2+0/v/dIpLVpv2jVv9zReSqVvMXWvPyROSR3uxDV3TQ/5+LyAHr//ivIhLbapn/9d8Yo19efOG5ga0AiLBerwLusr4vtuY9BdxnTd8PPGVNLwZW2t2HLvZ/LLAXiMRz8cA6PFd1/Qx4xGrzCPCYNX0N8A6ee0GmA9vs7sNF9HkOcAmwt9W8C+ov0B/It773s6b72d23LvR/NDAS+ADIajU/E/gMCAPSgcN4LvBwWdNDgVCrTabdfetC/xcAwdb0Y63+//2y/7oHcGGCgQjrXodI4CRwOZ7hLwBeAL5kTS+yXmMtny++Oiasd0YDW40xtcaYZuBD4Mv8Yz/b9v9F47EViBWRwb1ddFcYYz7Cc1Vbaxfa36uAtcaYcmNMBbAWWNjz1Xdde/03xuw3xrR3N/7nw74YYwqAc8O+fD6UjDGmETg37IvP66D/71m//+AZ5SDZmvbL/msAeMkYcxz4X+AYng/+KmAnUNnqF6L1UBf/MDyG1T6uN2vuZnuBOSISJyKReLZ4U4CBxpiTANb3AVb79oYQScL/XWh/A/XfoS0n9v+rePb6wE/7rwHgJetY7yI8u3eJQBRwdTtNz11X680QGn7DGLMfzy7vWuBdPLuy/zyU6v8JqP574UKHQwk0juq/iHwPz+//n87NaqeZz/dfA8B7VwAFxpgSY0wT8BdgBp5d/XM31LUe0qKj4TH8ljFmuTHmEmPMHDx9OQScPndox/pebDUP1OE+LrS/gfrv0JZj+m+dyP8CcJuxTgDgp/3XAPDeMWC6iERax/Ln47nDeQOe4S/AMxzG36zpjobH8FsiMsD6PgS4HniVf+xn2/7faV0dMx2oOnfoxM9daH/XAAtEpJ+1F7nAmhdoHDHsi3geZPUd4DpjTG2rRf7Zf7vPQvvTF/BD4ACe4+Ev4TnjPxTPf3Qe8GcgzGobbr3Os5YPtbv+buj/x3hC7zNgvjUvDngfz97A+0B/a77geejPYWAPra4Y8ZcvPAF3EmjCsyW39GL6i+dYcZ71dbfd/epi/79sTTcAp4E1rdp/z+p/LnB1q/nXAAetZd+zu19d7H8enmP6u6yvp/y5/zoUhFJKOZQeAlJKKYfSAFBKKYfSAFBKKYfSAFBKKYfSAFBKKYfSAFBKKYfSAFDKBiJy1u4alNIAUKqHtRoq5GLXFxHRv1XV7fSXSgUcEUkTkf0i8oz18JL3RCRCRD449xATEYkXkSPW9F0i8oaIvCkiBSLygIg8LCKfishWEel/np810Wpz7gEh5x4Q84GI/LeIfAg8aA0FsEVEdojIf7V5j3+35u8WkR+26cOTwCdAioj8UTwP5NkjIv/WM/96ykk0AFSgGg48YYwZA1QCX+mk/VjgVjzjt/8EqDXGTAK2AHeeZ70Xge8YY8bjGQLiP1stizXGzDXG/AL4NfB7Y8wU4NS5BiKywKp1KjARmCwic6zFI/E8Y2ASEA8kGWPGGmPGAc93+i+gVCc0AFSgKjDG7LKmdwJpnbTfYIypNsaU4Hl2w5vW/D0drSsiMXg+5D+0Zr2A5ylS56xsNT0Tz9gy4BlH6pwF1teneLb0R+EJBICjxvNwGfA8SWyoiPzWGpDsTCf9UapTXTo2qZQPa2g13QJE4Bm//dxGT/h52rtbvXZz8X8nNW1etzfwlgD/Y4z5wz/M9DxD+vP1jTEVIjIBzxPGlgE34RlkTqmLpnsAykmOAJOt6RvO084rxpgqoEJEZluz7sDzqMz2bMIzFDDAba3mrwG+KiJ9AEQk6dyw262JSDwQZIx5Hfh/eJ5Vq1SX6B6AcpL/BVaJyB3A+m56zyXAU9ZjMvOBuzto9yDwiog8CLx+bqYx5j0RGQ1ssR4ZfRa4Hc9eS2tJwPOtrgZ6tJvqVw6mw0ErpZRD6SEgpZRyKD0EpJQXROQJPFfytPZrY4xejqn8lh4CUkoph9JDQEop5VAaAEop5VAaAEop5VAaAEop5VD/H/CDLuIY3Cn1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['num_orders'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x83ee1b8688>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'week', 'center_id', 'meal_id', 'checkout_price', 'base_price',\n",
       "       'emailer_for_promotion', 'homepage_featured', 'num_orders'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['center_id', 'meal_id', 'checkout_price', 'base_price',\n",
    "       'emailer_for_promotion', 'homepage_featured',]]\n",
    "y = df['num_orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3, random_state =101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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